{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a09125b",
   "metadata": {},
   "source": [
    "### Figure 2: Marginal Effects on MID Initiation (Challenger) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83929409",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded. Shape: (1077992, 26)\n",
      "   challenger  target    year  dispute  ptargdef  pchalally  pchaloff  \\\n",
      "0        20.0     2.0  1920.0      0.0       0.0        0.0       0.0   \n",
      "1        20.0     2.0  1921.0      0.0       0.0        0.0       0.0   \n",
      "2        20.0     2.0  1922.0      0.0       0.0        0.0       0.0   \n",
      "3        20.0     2.0  1923.0      0.0       0.0        0.0       0.0   \n",
      "4        20.0     2.0  1924.0      0.0       0.0        0.0       0.0   \n",
      "\n",
      "   pchalneu   capprop  ln_distance  ...  contig   id  cold_war  majorpower  \\\n",
      "0       0.0  0.033794     6.120297  ...     1.0  1.0       0.0         1.0   \n",
      "1       0.0  0.039966     6.120297  ...     1.0  1.0       0.0         1.0   \n",
      "2       0.0  0.031848     6.120297  ...     1.0  1.0       0.0         1.0   \n",
      "3       0.0  0.034976     6.120297  ...     1.0  1.0       0.0         1.0   \n",
      "4       0.0  0.033888     6.120297  ...     1.0  1.0       0.0         1.0   \n",
      "\n",
      "   poor_rep_t_def  poor_rep_t_all  dispute_hh3  dispute_hh4  \\\n",
      "0             0.0             0.0          0.0          0.0   \n",
      "1             0.0             0.0          0.0          0.0   \n",
      "2             0.0             0.0          0.0          0.0   \n",
      "3             0.0             0.0          0.0          0.0   \n",
      "4             0.0             0.0          0.0          0.0   \n",
      "\n",
      "   l1_poor_rep_t_def  interwar  \n",
      "0                NaN       1.0  \n",
      "1                0.0       1.0  \n",
      "2                0.0       1.0  \n",
      "3                0.0       1.0  \n",
      "4                0.0       1.0  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ----------------------------------------------------------\n",
    "# 0) Lightweight installer for missing packages\n",
    "# ----------------------------------------------------------\n",
    "try:\n",
    "    import pandas as _pd\n",
    "    import matplotlib as _mpl\n",
    "    import statsmodels as _sms\n",
    "    import pyreadstat as _prs\n",
    "except Exception:\n",
    "    import sys, subprocess\n",
    "    subprocess.check_call(\n",
    "        [\n",
    "            sys.executable, \"-m\", \"pip\", \"install\", \"-U\",\n",
    "            \"pandas\", \"matplotlib\", \"statsmodels\", \"pyreadstat\"\n",
    "        ]\n",
    "    )\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf   # <-- needed for smf.logit\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# 1) Load dataset\n",
    "# ----------------------------------------------------------\n",
    "file_path = Path(\n",
    "    r\"C:\\Users\\rmuhi\\OneDrive\\Desktop\\Reputation_Conflict_Paper_All\\Second Order Reputation\\Replication files\\jpr_replication_data.dta\"\n",
    ")\n",
    "\n",
    "try:\n",
    "    data = pd.read_stata(file_path)\n",
    "except Exception as e:\n",
    "    print(\"Error reading Stata file:\", e)\n",
    "    raise\n",
    "\n",
    "print(\"Data loaded. Shape:\", data.shape)\n",
    "print(data.head())\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# 2) Pre-processing\n",
    "# ----------------------------------------------------------\n",
    "# Lag poor_rep_t_def by 1\n",
    "data['l1_poor_rep_t_def'] = data['poor_rep_t_def'].shift(1)\n",
    "\n",
    "# Drop rows with missing lag or dispute\n",
    "data = data.dropna(subset=['l1_poor_rep_t_def', 'dispute'])\n",
    "\n",
    "# Subset for ptargdef == 1\n",
    "data_sub = data[data['ptargdef'] == 1].copy()\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# 3) Model formulas\n",
    "# ----------------------------------------------------------\n",
    "formula_full = (\n",
    "    \"dispute ~ C(l1_poor_rep_t_def) + pchaloff + pchalneu + \"\n",
    "    \"capprop + majorpower + jdem + s_un_glo + ln_distance + \"\n",
    "    \"cold_war + peaceyrs + peaceyrs2 + peaceyrs3\"\n",
    ")\n",
    "formula_simple = \"dispute ~ C(l1_poor_rep_t_def)\"\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# 4) Fit logit models\n",
    "# ----------------------------------------------------------\n",
    "model1 = smf.logit(formula_full, data=data).fit(disp=False)      # Full sample + covariates\n",
    "model2 = smf.logit(formula_full, data=data_sub).fit(disp=False)  # Restricted sample + covariates\n",
    "model3 = smf.logit(formula_simple, data=data).fit(disp=False)    # Full sample, no covariates\n",
    "model4 = smf.logit(formula_simple, data=data_sub).fit(disp=False)  # Restricted sample, no covariates\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# 5) Prediction dataframes\n",
    "# ----------------------------------------------------------\n",
    "def make_pred_df(model_data, covariates, binary_var='l1_poor_rep_t_def'):\n",
    "    \"\"\"\n",
    "    Build a 2-row DF: binary_var = 0, 1, with all other covariates at their means.\n",
    "    \"\"\"\n",
    "    means = model_data.mean(numeric_only=True)\n",
    "\n",
    "    df = pd.DataFrame([means.copy(), means.copy()])\n",
    "    df[binary_var] = [0, 1]\n",
    "\n",
    "    # Ensure covariates are set (if present in data)\n",
    "    for var in covariates:\n",
    "        if var in model_data.columns:\n",
    "            df[var] = means[var]\n",
    "\n",
    "    return df\n",
    "\n",
    "covariates = [\n",
    "    'pchaloff', 'pchalneu', 'capprop', 'majorpower', 'jdem',\n",
    "    's_un_glo', 'ln_distance', 'cold_war',\n",
    "    'peaceyrs', 'peaceyrs2', 'peaceyrs3'\n",
    "]\n",
    "\n",
    "# With covariates\n",
    "pred_df1 = make_pred_df(data, covariates)\n",
    "pred_df2 = make_pred_df(data_sub, covariates)\n",
    "\n",
    "# No covariates (only the lagged reputation dummy)\n",
    "pred_df3 = pd.DataFrame({'l1_poor_rep_t_def': [0, 1]})\n",
    "pred_df4 = pd.DataFrame({'l1_poor_rep_t_def': [0, 1]})\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# 6) Get predictions (probabilities) + SEs\n",
    "# ----------------------------------------------------------\n",
    "def extract_mean_and_se(pred_frame):\n",
    "    \"\"\"\n",
    "    statsmodels .get_prediction().summary_frame() for logit typically\n",
    "    returns columns 'mean' (predicted prob) and 'mean_se'.\n",
    "    Convert to a simpler form with 'predicted' and 'se'.\n",
    "    \"\"\"\n",
    "    if 'mean' in pred_frame.columns and 'mean_se' in pred_frame.columns:\n",
    "        out = pred_frame[['mean', 'mean_se']].copy()\n",
    "        out = out.rename(columns={'mean': 'predicted', 'mean_se': 'se'})\n",
    "    else:\n",
    "        # Fallback: compute SE from CI width if needed\n",
    "        ci_low = pred_frame.iloc[:, pred_frame.columns.str.contains('lower')][\n",
    "            pred_frame.columns[pred_frame.columns.str.contains('lower')][0]\n",
    "        ]\n",
    "        ci_high = pred_frame.iloc[:, pred_frame.columns.str.contains('upper')][\n",
    "            pred_frame.columns[pred_frame.columns.str.contains('upper')][0]\n",
    "        ]\n",
    "        mean = pred_frame.iloc[:, 0]\n",
    "        se = (ci_high - ci_low) / (2 * 1.96)\n",
    "        out = pd.DataFrame({'predicted': mean, 'se': se})\n",
    "\n",
    "    # Ensure index 0 is \"No rep\" and 1 is \"Rep\"\n",
    "    return out.reset_index(drop=True)\n",
    "\n",
    "pred1_raw = model1.get_prediction(pred_df1).summary_frame()\n",
    "pred2_raw = model2.get_prediction(pred_df2).summary_frame()\n",
    "pred3_raw = model3.get_prediction(pred_df3).summary_frame()\n",
    "pred4_raw = model4.get_prediction(pred_df4).summary_frame()\n",
    "\n",
    "pred1 = extract_mean_and_se(pred1_raw)\n",
    "pred2 = extract_mean_and_se(pred2_raw)\n",
    "pred3 = extract_mean_and_se(pred3_raw)\n",
    "pred4 = extract_mean_and_se(pred4_raw)\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# 7) Prepare ordered predictions + titles for plotting\n",
    "# ----------------------------------------------------------\n",
    "# Order you requested:\n",
    "# 1) Full Sample (No Covariates)              -> pred3\n",
    "# 2) Full Sample (Additional Covariates)      -> pred1\n",
    "# 3) Restricted Sample (No Covariates)        -> pred4\n",
    "# 4) Restricted Sample (Additional Covariates)-> pred2\n",
    "\n",
    "ordered_preds = [pred3, pred1, pred4, pred2]\n",
    "ordered_titles = [\n",
    "    \"Full Sample (No Covariates)\",\n",
    "    \"Full Sample (Additional Covariates)\",\n",
    "    \"Restricted Sample (No Covariates)\",\n",
    "    \"Restricted Sample (Additional Covariates)\"\n",
    "]\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------\n",
    "# 8) Plot\n",
    "# ----------------------------------------------------------\n",
    "dot_color = '#e34a33'\n",
    "y_positions = [1, 0]  # y=1: No Reputation as Unreliable Ally, y=0: Reputation as Unreliable Ally\n",
    "\n",
    "plt.rcParams.update({\"font.family\": \"serif\", \"font.size\": 10})\n",
    "fig, axes = plt.subplots(2, 2, figsize=(10, 5), sharey=True)\n",
    "\n",
    "for ax, pred, title in zip(axes.flat, ordered_preds, ordered_titles):\n",
    "    ax.set_facecolor('#f9f9f9')\n",
    "    for spine in ['top', 'right', 'left', 'bottom']:\n",
    "        ax.spines[spine].set_visible(False)\n",
    "\n",
    "    # Plot with error bars\n",
    "    ax.errorbar(\n",
    "        pred['predicted'],\n",
    "        y_positions,\n",
    "        xerr=1.96 * pred['se'],\n",
    "        fmt='o',\n",
    "        color=dot_color,\n",
    "        ecolor=dot_color,\n",
    "        elinewidth=1,\n",
    "        capsize=2,\n",
    "        markersize=6\n",
    "    )\n",
    "\n",
    "    # Axis limits — preserve left margin for y-axis labels\n",
    "    right_limit = float((pred['predicted'] + 1.96 * pred['se']).max()) * 1.5\n",
    "    ax.set_xlim(0, right_limit)\n",
    "    ax.set_ylim(-0.6, 1.6)\n",
    "    ax.set_title(title, fontsize=10)\n",
    "\n",
    "    # Y-axis tick labels for ALL plots\n",
    "    ax.set_yticks(y_positions)\n",
    "    ax.set_yticklabels(\n",
    "        [\"No Reputation as Unreliable Ally\", \"Reputation as Unreliable Ally\"],\n",
    "        fontsize=10,\n",
    "        fontfamily='serif'\n",
    "    )\n",
    "    ax.tick_params(axis='y', length=0)\n",
    "\n",
    "    ax.grid(True, axis='x', linestyle=':', linewidth=0.7, alpha=0.6)\n",
    "\n",
    "plt.tight_layout(rect=[0.04, 0.05, 1, 0.96])\n",
    "fig.subplots_adjust(hspace=0.45)\n",
    "plt.suptitle(\"\", fontsize=12, fontfamily='serif')\n",
    "for ax in axes.flat:\n",
    "    plt.setp(ax.get_xticklabels(), rotation=25, ha='center')\n",
    "    \n",
    "plt.savefig(\n",
    "    r\"C:\\Users\\rmuhi\\OneDrive\\Desktop\\Reputation_Conflict_Paper_All\\Second Order Reputation\\Replication files\\figure_2.png\",\n",
    "    dpi=300,\n",
    "    bbox_inches=\"tight\"\n",
    ")\n",
    "plt.show()\n"
   ]
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